The impact of the National Science Foundation’s Innovation Corps (I-Corps) on academic innovation and entrepreneurship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract: In 2011, the U.S. National Science Foundation created the Innovation Corps (I-Corps) program in an effort to explore ways to translate the results of the academic research the agency has funded into new products, processes, devices, or services and move them to the marketplace. The agency established a 3-tier structure to support the implementation of the I-Corps concept. Selected I-Corps teams consisting of the principal investigator, an entrepreneurial lead, and an industry mentor participate in a 7-week accelerated version of the Lean Launchpad methodology that was first developed by Steve Blank at Stanford University. Participating teams engage in talking to potential customers, partners, and competitors and address the challenges and the uncertainty of creating successful ventures. I-Corps sites were set up to promote selected aspects of innovation and entrepreneurship ecosystems at the grantee institutions. I-Corps Regional Nodes were charged with recruiting I-Corps teams in a larger geographical area as well as stimulating a new culture of academic entrepreneurship in the institutions in their area of influence. This Topical Review describes the experiences and the impact of the New York City Regional Innovation Node, which is led by the City University of New York, in partnership with New York University and Columbia University.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it